Detecting Ripe Canarium Ovatum (Pili) Using Adaboost Classifier and Color Analysis

Abstract

Bicol region in terms of agriculture is known for its indigenous crop, canarium ovatum, or most commonly known as ‘Pili.’ Canarium Ovatum has been recognized for its economic importance due to its potential in the export market. However, there has been a growing demand for pili because of the lack of equipment in post-production and processing operations and the need of the market cannot be met by the growers and processors. Fruit Detection in harvesting one of the post-production processes, is the first major task of a robot. A vision system that can easily recognize fruit in a tree, which is levelled to be as intelligent as human beings is difficult to develop. This study helped increase the accuracy of the detection of ripe canarium ovatum. Images were captured using a high-end drone (Phantom 4 Professional with 20 megapixel resolution). This study established the data set by selecting images for training which is composed of 80% of the total image acquired and 20% for test set. The background information of the images like leaves, twigs, unripe pili, and other objects were also categorized. An Adaboost classifier and color analysis was used in the detection of ripe pili. An average of 90.77% accuracy of the ripe pili detection was recognized during the evaluation of the algorithm. The performance of the algorithm was evaluated according to true positive, false negative, and false positive with an average of 90.77%, 9.23%and 0.77% results, respectively. The detection algorithm achieved a high correct detection rate and the Haar-like features have potentials for extracting shape and texture information of the ripe pili in natural settings which contain various visual features due to complex structures of the leaves, twigs and other objects. Future research will include enhanced detection rates, reduced processing time, reduced manual processes, and various cultivated varieties of pili. It may also accommodate more varied unstructured environments.